Final Report for Dynamic Models for Causal Analysis of Panel Data. The Impact of Measurement Error in the Analysis of Log-Linear Rate Models: Monte Carlo Findings. Part III, Chapter 4.

Carroll, Glenn R.; And Others

This document is part of a series of chapters described in SO 011 759. The chapter advocates the analysis of event-histories (data giving the number, timing, and sequence of changes in a categorical dependent variable) with maximum likelihood estimators (MLE) applied to log-linear rate models. Results from a Monte Carlo investigation of the impact of measurement error on the performance of MLE applied to a log-linear rate model are reported. Sections of the document describe constant rate models of both a single event and a multiple event, examine complications created by errors in recording the timing of events, focus on the same complication for the multivariate rate model, and consider consequences of measurement error in the exogenous variables for this model. Results indicate that the performance of MLE in rate models is good. Also, measurement error in the waiting time and in exogenuous variables causes only minimal deterioration in estimator quality. The studies suggest that the most pronounced effects of measurement error can be avoided if the investigator does not impose arbitrary censoring points upon complete event-histories. When data on the timing of natural events are available, they should be used in entirety. (Author/KC)